Seismic Modelling and Pattern Recognition in Oil Exploration

1. Front Matter

   Pages i-viii

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2. Introduction

   • Amita Sinvhal, Harsha Sinvhal

   Pages 1-10

3. Monte Carlo Models in Oil Exploration

   • Amita Sinvhal, Harsha Sinvhal

   Pages 11-32

4. Morkov Chain Models in Oil Exploration

   • Amita Sinvhal, Harsha Sinvhal

   Pages 33-61

5. Simulation of Synthetic Seismograms

   • Amita Sinvhal, Harsha Sinvhal

   Pages 63-90

6. Feature Extraction from Seismograms

   • Amita Sinvhal, Harsha Sinvhal

   Pages 91-111

7. Statistical Analysis of Seismic Data

   • Amita Sinvhal, Harsha Sinvhal

   Pages 113-127

8. Patterns Recognized from Synthetic Seismic Data

   • Amita Sinvhal, Harsha Sinvhal

   Pages 129-144

9. Recognized Patterns and Seismic Reflection Data

   • Amita Sinvhal, Harsha Sinvhal

   Pages 145-164

10. Back Matter

    Pages 165-178

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The reasons for writing this book are very simple. We use and teach com­ puter aided techniques of mathematical simulation and of pattern recogni­ tion. Life would be much simpler if we had a suitable text book with methods and computer programmes which we could keep referring to. Therefore, we have presented here material that is essential for mathematical modelling of some complex geological situations, with which earth scientists are often confronted. The reader is introduced not only to the essentials of computer modelling, data analysis and pattern recognition, but is also made familiar with the basic understanding with which they can plunge into when solving related and more complex problems. This book first makes a case for seismic stratigraphy and then for pattern recognition. Chapter 1 provides an extensive review of applications of pattern recognition methods in oil exploration. Simulation procedures are presented with examples that are fairly simple to understand and easy to use on the computer. Several geological situations can be formulated and simulated using the Monte Carlo method. The binary lithologic sequences, discussed in Chapter 2, consist of alternating layers of any two of sand, shale and coal.

• digital elevation model
• pattern
• pattern recognition
• prospecting
• seismic

• Department of Earthquake Engineering, University of Roorkee, India

  Amita Sinvhal

• Department of Earth Sciences, University of Roorkee, India

  Harsha Sinvhal

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